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309 results for “swarm”
Generic, Scalable and Decentralized Fault Detection for Robot Swarms
<p>This raw data archive includes the data on fault detection in a simulated swarm of 20 e-puck robots. The data was used in the paper Generic, Scalable and Decentralized Fault Detection for Robot Swarms by D. Tarapore et al. (2017).</p> <p>See readme.txt for more details.</p>
Swarm Check-ins
<p>This database was created and collected with the goal of observing patterns in urban mobility. To achieve this, the Twitter API was used to collect public check-ins made by users via Foursquare Swarm. </p><p>check-in.csv description:</p><ul><li><strong> venueID:</strong> Foursquare venue identifier</li><li><strong> userID:</strong> Swarm user identifier</li><li><strong>venueName:</strong> Name of the venue where the check-in was made</li><li><strong>category:</strong> Category of the venue where the check-in was made</li><li><strong>country:</strong> Country of the venue where the check-in was made</li><li><strong>city:</strong> City of the venue where the check-in was made</li><li><strong>timestamp:</strong> Time when the check-in was shared on Twitter</li><li><strong>latitude:</strong> Latitude of the venue where the check-in was made</li><li><strong>longitude:</strong> Longitude of the venue where the check-in was made</li></ul><p>The file categories.json contains information from all Foursquare categories.</p><p> </p><p> </p><p> </p>
Spectral sensitivity transition in the compound eyes of a twilight-swarming mayfly and its visual ecological implications
<p>Aquatic insect species that leave the water after larval development, such as mayflies, have to deal with extremely different visual environments in their different life stages. Measuring the spectral sensitivity of the compound eyes of the virgin mayfly (Ephoron virgo) resulted in differences between the sensitivity of adults and larvae. Larvae were primarily green-, while adults were mostly UV-sensitive. The sensitivity of adults and larvae were the same in the UV, but in the green spectral range, adults were 3.3 times less sensitive than larvae. Transmittance spectrum measurements of larval skins covering the eye showed that the removal of exuvium during emergence cannot explain the spectral sensitivity change of the eyes. Taking numerous sky spectra from the literature, the ratio of UV and green photons in the skylight was shown to be maximal for θ ≈ − 13° solar elevation, which is in the θmax = -14.7° and θmin = -7.1° typical range of swarming that was established from webcam images of real swarmings. We suggest that spectral sensitivity of both the larval and adult eyes are adapted to the optical environment of the corresponding life stages.</p>
FIG. 6 in An emic understanding of honey bees and their environment: attracting bee swarms to nest on rafters in Belitung, Indonesia
FIG. 6. — Two views on the same rafter (sunggau muke with renak ngelandas; 28 March 2017). Credits: M. Rhomadona (A), N. Césard (B).
FIG. 5 in An emic understanding of honey bees and their environment: attracting bee swarms to nest on rafters in Belitung, Indonesia
FIG. 5. — Rendap rabas, before (A) and after (B) being improved (November 2013). The arrows indicate the cuts in the vegetation. Credits: N. Césard.
FIG. 1 in An emic understanding of honey bees and their environment: attracting bee swarms to nest on rafters in Belitung, Indonesia
FIG. 1. — Hemispherical photographs of three rendap (in pale blue): direct (A), indirect (or semi-open) (B) and well (C) access paths. Credits: N. Césard.
rDNA 18S V9 metabarcoding tables (Swarm) for Tara Oceans Expedition (2009-2013), including Tara Polar Circle Expedition (2013)
<p>Reads were grouped into OTUs using the following swarm-based pipeline: paired-end reads were merged with vsearch’s --fastq_mergepairs command (version 2.15.1, allowing for staggered reads; Rognes et al., 2016), and trimmed with cutadapt (version 3.0; Martin, 2011), keeping only reads containing both forward and reverse primers. After trimming, the expected error per read was estimated with vsearch’s command --fastq_filter and the option --eeout. Each sample was then de-replicated, i.e. strictly identical reads were merged, using vsearch’s command --derep_fulllength, and converted into fasta format. Clustering was performed at the sample level with swarm 3.0 using default parameters (Mahé et al., 2015). Prior to global clustering, individual fasta files (one per sample) were pooled and further dereplicated with vsearch. Files containing per-read expected error values were also dereplicated to retain only the lowest expected error for each unique sequence. Global clustering was performed with swarm (using the fastidious option). Cluster representative sequences were then searched for chimeras with vsearch’s command --uchime_denovo using default parameters (Edgar et al., 2011).</p> <p>Clustering results, expected error values, taxonomic assignments, and chimera detection results were used to build a “raw” occurrence table. Reads without primers, reads shorter than 32 nucleotides and reads with uncalled bases (“N”) were discarded. For a “filtered” occurrence table, non-chimeric sequences, sequences with an expected error per nucleotide below 0.0002, and clusters containing at least 2 reads were retained. Since primer trimming is not perfect, some sequences can still contain primer fragments or be excessively trimmed. These sub- or super-sequences were identified using vsearch and merged with their closest, most abundant perfectly trimmed sequence. Finally, occurrence patterns throughout our sample collection were used to further refine the occurrence table. Clusters that contain sub-clusters with only a single-nucleotide difference but with different ecological patterns (defined here as uncorrelated abundance values in at least 5% of the samples) were turned into distinct clusters (https://github.com/frederic-mahe/fred-metabarcoding-pipeline). On the other hand, clusters with similar sequences that had correlated abundance values in at least 95% of the samples, were merged using a re-implementation of lulu's method (Frøslev et al. 2017; https://github.com/frederic-mahe/mumu).</p> <p> </p>
Fig. 1 - MSNM i29340 in Short Communication Evidence of mysid swarm behaviour (Crustacea: Malacostraca) from the Cenomanian (Late Cretaceous) of Hakel, Lebanon
Fig. 1 - MSNM i29340. The mass mortality event, natural light. (x 0.6). / L'evento di mortalità di massa, luce naturale. (x 0.6).
Fig. 2 - MSNM i29340. Selected specimens. A in Short Communication Evidence of mysid swarm behaviour (Crustacea: Malacostraca) from the Cenomanian (Late Cretaceous) of Hakel, Lebanon
Fig. 2 - MSNM i29340. Selected specimens. A) close-up of morphotype 1. (x4). B) morphotype 1 (T1) and morphotype 2 (T2), natural light. (x 1.6) Abbreviations: cxp) carapace, th) thorax, pl) pleon, t) telson. / Esemplari selezionati. A) ingrandimento del morfotipo 1. (x4). B) morfotipo 1 (T1) e morfotipo 2 (T2), luce naturale. (x 1.6) Abbreviazioni: cxp) carapace, th) torace, pl) addome, t) telson.
Figures -using Particle Swarm Optimization (PSO)-An Optimized Clustering Approach for Automated Detection of White Matter Lesions in MRI Brain Images
<p>The performance of WML quantification is evaluated using clustering algorithms. When the<br> image is pre-processed, contrast of the image is enhanced. The resulting enhanced image is<br> clustered using the effective clustering algorithms. Figure 3 represents the input image for WML<br> detection. In order to increase robustness, the noisy medical image is pre-processed. Figure 4<br> depicts the pre-processed image. Bright contrast stretching, which is one of the image enhancement<br> (pre-processing) techniques is applied. After pre-processing the enhanced image is subjected to<br> clustering. Three clustering models are proposed to provide accurate results.</p> <p>All scans obtained from different image clustering models are manually ranked based on<br> values in table 1. Table 2 represents WML detection rates of optimized images. FCM, GPC and<br> GFCM clustering methods and hybrid optimized methods (FCM-PSO, GPC-PSO and GFCM-PSO)<br> are applied on a dataset of 208 images and ranking is done in terms of under detected, over<br> detected, properly detected as shown in figure 8 and figure 9. The number of images detected<br> properly in GFCM is comparatively high than FCM and GPC.</p>
Figure 2. Some screenshots from the software system-Design and Development of a Software System for Swarm Intelligence Based Research Studies
<p>All of the mentioned operations can be performed easily by using the provided controls over<br> the related interfaces – windows of each algorithm. It is also important that each algorithm interface<br> is supported by visual controls to view obtained results with typical iteration-based graphics or<br> problem oriented visual elements. For instance, resulting graph structures are automatically shown<br> by the algorithm interfaces after solving some specific, popular problems like Travelling Salesman<br> Problem (TSP), Vehicle Routing Problem (VCP)…etc. Visually improved using features and<br> functions of the software system are critical aspects to provide more effective and useful platform to<br> perform SI based research studies better.<br> Related to the designed and developed software system, some screenshots from the software<br> system [interfaces of two algorithms (IWDs and ABC)] are represented in Fig. 2.</p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 25: The fifth obstacle with 100 robots after passing all robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 24: The fifth obstacle with 100 robots after passing some robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 23: The fifth obstacle with 100 robots before passing any robot
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 22: Fifth Obstacle (obstacle with two entries that each allow the passing of one robot)
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 21: The fourth obstacle with 100 robots after passing all robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 19: The fourth obstacle with 100 robots before passing any robot
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 15: third obstacle with 100 robots before passing any robot
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance.</p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 14: Illustrate the Path of Move for robots on swarm robotics with the third obstacle type
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance</p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 13: second obstacle with 100 robots after passing all robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.